Journal article Open Access
Mazziotti Raffaele; Carrara Fabio; Viglione Aurelia; Lupori Leonardo; Lo Verde Luca; Benedetto Alessandro; Ricci Giulia; Sagona Giulia; Amato Giuseppe; Pizzorusso Tommaso
Pupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however, its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs online pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for the non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both locomotor-induced and stimulus-evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.